Automatic classification and pathological staging of confocal laser endomicroscopic images of the vocal cords

Abstract

Confocal laser endomicroscopy is a novel imaging technique which provides real-time in vivo examination and histological analysis of tissue during an ongoing endoscopy. We present an automatic classification system that is able to differentiate between healthy and cancerous tissue of the vocal cords. Textural as well as CNN features are encoded using Fisher vectors and vector of locally aggregated descriptors while the classification is performed using random forests and support vector machines. Two experiments are investigated following a leave-one-sequence-out cross-validation and a fixed training and test set approach. Classification rates reach up to 87.6 % and 81.5 %, respectively.